Proceedings of the Sixth Australian Conference on Neural Networks
Sydney, 6-8 February 1995, pages 152-155.
Baked Product Classication with the Use of a Self-Organising Map
T.RayChaudhuriy
yDepartment
J.Chien-Hong Yehy
L.G.C.Hameyy
C.T. Westcottz
of Computing, School of MPCE, Macquarie University, NSW 2109, Australia
zArnott's Biscuits Limited, Homebush, NSW 2140, Australia
Abstract
Study of the baking of biscuits involves among other
aspects detailed analysis of colour changes in the product during the process. Previous study has shown the
existence of a colour development curve (known as the
baking curve) by examining colour development in the
RGB and HSI colour spaces.
In the current work a dierent approach to extracting the baking curve is presented. Using a Kohonen
self-organising map with an optimum number of output
nodes a well-dened baking curve is automatically extracted from preprocessed data of images gathered during the actual baking process. We propose that these
curves can be used as a basis for characterising the
colour bake level of a biscuit.
1 Introduction
In order to develop an insight into the process of baking
it is important to study the colour changes in the baked
product [2]. One approach has been to gather digitized
colour image data of biscuits at various stages of baking
and to plot the colour pixel values in an RGB or HSI
colour cube. Such a plot graphically illustrates how
colour development occurs during baking and has been
called the baking curve [5].
A method of assessing the bake level with the use of
a backpropagation neural network has also been implemented [7]. Intensity histograms of biscuit images have
been used as input data to the neural network. The
intensity feature of the biscuit images has been used
as the criterion for assessment and the backpropagation neural network has been trained to classify these
biscuit images. There is scope for improvement in the
degree of success of this classication. This is because
the classication of biscuits according to bake levels
should take into account the overall colour development and not merely the intensity.
The baking curve is a one-dimensional representation of the important colour variations within a threedimensional data space. In order to reduce the dimensionality of data in biscuit colour bake level assessment, measurements should be made along this curve.
We have devised a means to automatically extract the
baking curve, using a self-organising map [4]. We suggest that points on the output of such a map should
be used as colour bake level bins for preparing a histogram to characterise specic cases of biscuit image
data. These histograms would then be used as input
to a backpropagation neural network trained to classify
them in a manner similar to that in the earlier work
on intensity assessment [7].
2 The Self Organising Map
Kohonen's Self Organising Map (SOM) is an unsupervised learning technique that can be used very eectively for extracting structure of complex experimental
data. The SOM uses a vector quantisation algorithm
that produces a mapping from a high dimensional data
space on to a one- or two- dimensional lattice of output
nodes. During training the SOM learns the relative ordering of the input data [3]. This kind of network can
therefore can be an extremely useful tool for the analysis of complicated experimental data where the data
elements bear highly nonlinear relationships to one another.
The SOM networks used in our experiments have
three input nodes and between ten and thirty output
nodes. The three input nodes represent the threedimensional Red(R), Green(G) and Blue(B) colour
components of pixel values from a digitised colour image of a biscuit. The network is designed to have output nodes in the form of a sequential string, i.e., a one
dimensional lattice of output nodes and every node in
the input layer is connected to each node in the output
layer. Such a network will, upon training, map the entire set of RGB pixel values from a biscuit image on to
a one dimensional array of points (or output nodes).
This has two main eects. Firstly the input data function is compressed into a data space of lower dimensionality and secondly the inter-relationships between
the most relevant points in the input data are retained
intact in the output format of the network. The network's internal learning rule enables it to extract the
inter-relationships without supervision, unlike a backpropagation neural network [1].
Copyright  1995 by T. RayChaudhuri, J. Yeh, L. Hamey and C. Westcott.
All rights reserved.
3 Preprocessing of Biscuit Image Data
200
GREEN (z)
100
200
0
200
100 BLUE (x)
RED (y) 100
00
Figure 1: A baking curve of Sao biscuits extracted
by using a SOM with 30 output nodes and 20 passes
through the total training data. The training data
pixels have been plotted in the background in RGB
space.
200
GREEN (z)
100
Training passes
.
1
20
200
0
200
100 BLUE (x)
RED (y) 100
00
Figure 2: Visualisation of the Sao Baking Curve obtained by using a SOM with 10 output nodes and varying the number of passes through the training data.
Four biscuit samples of the \Sao" variety from Arnott's
Biscuits Ltd were digitized. It was ensured that these
samples ranged from the underbaked to the overbaked
category with varying levels of baking in between. The
image acquisition setup consisted of a camera positioned vertically over a zone lit by two daylight colourbalanced Thorn uorescent lamps. A three-chip CCD
camera was used to capture individual colour images of
each biscuit. These images were digitized with the use
of a frame grabber board mounted within a microcomputer. The image data was subsequently calibrated for
variations in illumination levels and the biscuit data
was cropped from the image yielding 47 967 pixels per
biscuit with three separate R, G and B values to each
pixel. These data from all 4 biscuits, totalling 191 868
pixels, were then shued into a random sequence and
given as input data to the SOM_PAK program [6] used in
this investigation. Each pixel formed a separate input
pattern to the network.
Another set of similar experiments was conducted
with the \Milk Coee" brand of biscuits from Arnott's.
See sections 4 and 5 for details.
4 Training of the SOM and Visualisation of Results
A SOM with three input nodes and an output layer
of between ten and thirty nodes arranged in a one dimensional sequence was chosen. The neighbourhood
function was the bubble function dened in [6]. Learning rates ranged between 0.02 and 0.05. We show in
gure 1 a plot of a SOM with 30 output nodes and
20 passes through the training data superimposed in
RGB space upon a display of colour pixels from the
Sao biscuits that were used to train the SOM. Clearly,
the SOM nodes follow the bake colour development.
Figure 2 shows the appearance of the SOM baking
curve for Sao biscuits with 1 and 20 training passes
through the entire set of input data and 10 output
nodes. Clearly the SOM requires several training
passes through the data to obtain the points on the
extreme ends of the curve. Between 5 and 20 training
passes the changes were small. After 20 passes it was
found that there was no further signicant change.
Figure 3 shows the appearance of the Sao baking
curve extracted with a SOM with 10 and 30 output
nodes, each time with 20 training passes. It can be
seen that with more output nodes the extremes of of
the curve are better represented. If the number of output nodes is continually increased, however, then the
baking curve extracted with the SOM begins to lose
its smoothness.
200
GREEN (z)
100
Output nodes
.
10
200
0
200
30
100 BLUE (x)
RED (y) 100
00
Figure 3: The SOM Baking Curve for Sao biscuits with
20 training passes and varying the number of output
nodes.
200
GREEN (z)
100
200
0
200
100 BLUE (x)
RED (y) 100
00
Figure 4: Visualisation of the Milk Coee Baking
Curve obtained by using a SOM with 20 output nodes
and 20 passes through the training data. The actual
colour pixels of the biscuits have been plotted in the
background.
We observe also that with a larger number of output
nodes the network tends to produce a greater number
of points bunched very close together in the central
zone of the curve and a more sparse distribution of
points at the two ends. This results from the concentration of the training data in the centre of the curve
and the property of the SOM that it reects the probability density function of the data [6]. Training the
network for a more even distribution of these points is
the subject of ongoing investigation.
The SOM also successfully extracts the baking curve
for other products such as Milk Coee biscuits (gure 4). The SOM used here has 20 output nodes and
has been passed through the training data 20 times. In
gure 4 the SOM has been shown against a background
of biscuit colour pixels plotted in RGB space to demonstrate the eectiveness of the method. The structure
of the baking curve is similar to that found for Sao
biscuits, but occupies a dierent location in the colour
cube, reecting the dierences in ingredients between
the products. The colour curve for Milk Coee biscuits
is also shorter than the Sao baking curve because individual Milk Coee biscuits exhibit consistent browning
whereas Sao biscuits have blisters which cause uneven
browning.
5 Interpretation of Results and
Scope for Future Work
Each set of input values to the SOM is the vector of
RGB values of a single pixel. The total set of input
data to the SOM is a randomised collection of such
pixel values ranged over the entire baking process|
from an underbaked to an overbaked biscuit. The
number of biscuit samples chosen to cover the range
depends largely on the type of product. In the case
of Sao biscuits four cropped biscuit images having approximately 48000 pixels each were sucient for bake
curve extraction. For Milk Coee biscuits which have
more uniform colour distribution than Sao we used a
total of 298 biscuit samples and each of these had a
10x20 window of pixels cropped from its central portion. The visualised ouput nodes of the trained SOM,
plotted in an RGB colour cube, clearly show the process of colour development. We thus obtain the baking
curve automatically by training a self organising map
upon a set of data collected from a baking process.
This curve can serve as a graphical representation of
the process. It could also be used as a basis for developing histograms that will characterise the colour
bake levels of specic cases of biscuit image data. This
can be done by using points on the baking curve as
the bins to compute the histogram of a biscuit image.
Future work of the project will be in this direction and
the histograms will then be used as input data to a
Colour Bake Level
Feedforward Neural Network
Histogramming
SOM
Biscuit Image
Figure 5: Flowchart of the Colour Bake Level Assessment Process using SOM
supervised learning process such as a backpropagation
neural network that has been trained to classify the
colour bake level of a biscuit (see gure 5). In comparison to using standard RGB histograms such a method
of histogramming would signicantly reduce the number of input nodes to the feedforward neural network
classier. The feasibility of using a backpropagation
neural network for a similar classication task involving intensity levels of monochrome biscuit images, has
already been demonstrated [7].
6 Conclusion
We have successfully shown that a Kohonen Self Organising Map can be used to automatically extract a
baking curve from image data of biscuits at dierent
stages of baking. The variations in the conguration
of the baking curve with dierent training parameters
have been discussed. We have also dened a means of
determining the colour bake level of a biscuit by using
points on the baking curve as colour level bins of a histogram. With such histograms as input data we will
be able to train a backpropagation network to classify
biscuits according to their colour bake levels on the
lines of our earlier work on monochrome images [7].
This method of automated classication of baked
biscuits according to their colour levels would be of
enormous benet to the manufacturer. Manual inspection by human inspectors is prone to both short-term
and long-term variation in judgement [7]. Such drifts
and variations in product quality due to the lack of objective standards of comparison [2] would be eectively
eliminated with the introduction of a computerised inspection system.
7 Acknowledgement
We would like to thank Arnotts Biscuits Ltd. for their
continuing support in this research project, especially
with regard to providing data, technical facilities and
nancial support.
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